Indian monsoon precipitation isotopes linked with high level cloud cover at local and regional scales

Earth and Planetary Science Letters 529 (2020) 115837

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Earth and Planetary Science Letters www.elsevier.com/locate/epsl

Indian monsoon precipitation isotopes linked with high level cloud cover at local and regional scales Di Wang a,b , Lide Tian a,b,c,∗ , Zhongyin Cai a,b , Lili Shao a,b , Xiaoyu Guo d , Ran Tian a,b , Yike Li a,b , Yiliang Chen a,b , Chuan Yuan a,b a

Institute of International Rivers and Eco-security, Yunnan University, Kunming, Yunnan 650500, China Yunnan Key Laboratory of International Rivers and Transboundary Eco-security, Kunming 650500, China c CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China d Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China b

a r t i c l e

i n f o

Article history: Received 2 February 2019 Received in revised form 6 September 2019 Accepted 9 September 2019 Available online xxxx Editor: F. Moynier Keywords: precipitation isotopes cloud cover convective activity regional scale southern Tibetan Plateau

a b s t r a c t Precipitation stable isotopes preserve historic changes of evaporation in the source regions and precipitation processes, therefore, they can be used to reveal regional hydrological cycle dynamics and paleoclimate reconstructions. In monsoon regions, strong inverse impacts of convection on precipitation isotope ratios, have created a debate regarding the interpretation of isotope records as local climate proxies. The proportions of stratiform to convective precipitation on water isotopes, together with the influence mechanisms on seasonal and interannual scales remain highly uncertain. To further address the influence of precipitation patterns on water isotopes, we used 10 yrs of precipitation isotope data from the southern Tibetan Plateau (TP) to explore the effects of large-scale cloud cover and local climate on precipitation isotopes. Correlation analysis performed between local precipitation δ 18 O values and different level cloud data, indicated significant negative correlations between precipitation isotopes and high level cloud cover on both seasonal and interannual time scales. This result suggests that highlevel convection in the upper moisture transport stream is a main control on precipitation isotopes in the southern TP. The clear and coherent variations of precipitation isotopes with the Southern Oscillation Index and outgoing longwave radiation confirmed that strong convection activity in the moisture source region and during transport significantly depleted heavy isotopes in vapor, producing substantially decreased precipitation δ 18 O in the study region. These results agree with earlier findings of tree ring cellulose isotope records that correlate with cloud cover, but we emphasized the important role of larger-scale regional cloud cover. We also delineated different maximum correlation zones for seasonal and interannual time scales, likely due to different mechanisms. These findings further improve the interpretation of paleoisotope records from the Indian summer monsoon region. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Stable isotopic signals (δ 18 O and δ 2 H) preserved in natural archives, such as ice cores (Thompson et al., 2000), tree-ring cellulose (Liu et al., 2017), stalagmites (Cheng et al., 2012), and lake deposits, are important proxies for reconstructing past climate and hydrological cycles. However, it is critical to understand the mechanism underlying current isotopic changes. Many efforts have been made to clarify the factors controlling precipitation isotopes in low and middle latitude regions with a strong monsoon influence

*

Corresponding author at: Institute of International Rivers and Eco-security, Yunnan University, Kunming, Yunnan 650500, China. E-mail address: [email protected] (L. Tian). https://doi.org/10.1016/j.epsl.2019.115837 0012-821X/© 2019 Elsevier B.V. All rights reserved.

(Thompson et al., 2000; Tian et al., 2003). However, it remains open to debate whether local or regional mechanisms control precipitation isotopes. Earlier work focused on local effects on precipitation isotopes during the precipitation process, e.g., temperature and precipitation amount (Dansgaard, 1964). The inverse relationship between monthly δ 18 O and precipitation amounts (termed the “amount effect”) is significant in low-latitude and monsoon regions (Dansgaard, 1964). However, more studies have suggested that precipitation isotopes in tropical regions are less related to local meteorological parameters, but more influenced by regional integrated convective activity (Bony et al., 2008; Breitenbach et al., 2010; Kurita, 2013; Rahul et al., 2016; Risi et al., 2008b). Indeed, there is increasing recognition that regional convective activity is the primary driver for precipitation isotopes at low

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Fig. 1. Location of the Yamdruk-tso basin. Red circles are the precipitation sampling stations and the black triangle is the meteorological station. (For interpretation of the colors in the figure(s), the reader is referred to the web version of this article.)

latitudes (Bowen et al., 2019; Galewsky et al., 2016). Significant depletion of precipitation δ 18 O values in clouds only occurs during large-area events (Lekshmy et al., 2014). On a seasonal scale, the precipitation isotope composition records the abrupt increase of convective activity during the monsoon onset and the integrated regional-scale convection variability over subsequent days (Risi et al., 2008b). Regarding interannual variations, precipitation isotopes in the Asian monsoon region primarily reflect distillation during transport from source regions, and are also governed by large-scale tropical variability (Ishizaki et al., 2012). Interannual isotope variations are closely related to the El Niño-Southern Oscillation (ENSO) in Asian (Yang et al., 2018) and Indian monsoon region (Krishnamurthy and Goswami, 2000; Rahul et al., 2016). The underlying mechanism is that weaker convection in the broader moisture source region of the Indo-Pacific region in El Niño years is linked to less depletion of vapor isotopes and the subsequent precipitation isotopes observed in large monsoon precipitation regions (Cai et al., 2017). Therefore these isotopes are weakly related to local precipitation amounts, but more strongly related to largescale monsoon intensity (Yang et al., 2016). Recent research has shown that the stratiform fraction is inversely related to precipitation isotopes due to micro processes during rainfall formation (Aggarwal et al., 2016), highlighting the critical impact of precipitation patterns (stratification and convection) on precipitation isotopes in mid-low latitude regions. However, in the Indian monsoon region, convection precipitation is highly related to depleted precipitation isotopes (Cai et al., 2017; He et al., 2015; Lekshmy et al., 2014; Torri et al., 2017). This is particularly true in tropical cyclone regions where condensation forms in higher cloud with depleted isotopes resulting in a reverse correlation between precipitation isotopes and large-scale regional cloud top height (Cai and Tian, 2016; Deshpande et al., 2010). Cloud cover is likely related to precipitation isotope variations especially via convective rainfull. A tree ring δ 18 O time series rebuilt from southeastern Tibet indicated that the isotope signal is strongly correlated with regional summer cloud cover (Shi et al., 2012). Outgoing longwave radiation (OLR), which cloud cover tends to reduce to below clear sky values by absorption (Susskind

et al., 2011), is negatively correlated with the intensity of convection in low latitude regions (Wang and Xu, 1997). Precipitation δ 18 O values in Asian monsoon regions are significantly correlated with OLR (Cai et al., 2017; Yang et al., 2018) in that deep convection leads to intense rainout from precipitation and depleted precipitation isotopes (Bony et al., 2008; Liebmann, 1996; Risi et al., 2008a, 2008b). To analyze the relationship between regional cloud cover and Indian monsoon precipitation isotopes, we evaluated 10 yrs of precipitation isotope data from the Yamdruk-tso basin in the southern Tibetan Plateau (TP). We considered local- and regional-scale influences and, in particular, the effects of different levels of cloud cover. The purpose of this study is twofold. Firstly, we aim to clarify the influence of cloud cover at different height levels, an indicator of convective intensity, on precipitation isotopes, and the potential mechanisms affecting the seasonal and interannual variations of precipitation isotopes. Secondly, we aim to quantitatively evaluate the local and regional controls on precipitation isotopes. Our research site is in the northern Himalayas with an elevation of over 4400 m a.s.l. The precipitation isotopes at such high altitudes can be applied to the isotope records of middle Himalayan glaciers as these high alpine glaciers preserve high-resolution ice core isotope signals (Thompson et al., 2000). In this sense, this study has wider scientific significance with regard to paleoclimate reconstruction. 2. Geophysical location and data acquisition 2.1. Geophysical location The Yamdruk-tso basin (90◦ 08-91◦ 45 E and 28◦ 27-29◦ 12 N, Fig. 1), with an area of approximately 1037 km2 , is the largest closed lake basin between the Himalayas in the south and Yalongzangbo River to the north. The lake is composed of several narrow connected lakes with an overall lake surface of approximately 621 km2 , a lake water volume of approximately 16.0 km3 , and a maximum depth of 55 m (Tian et al., 2008). It is fed by summer monsoon precipitation and some glacier meltwater. The basin

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has a large elevation range from approximately 4440 m at the lake surface to 7206 m on the glacier’s summit. Based on meteorological data from the Langkazi meteorological station located within the basin, from 2004 to 2016, the annual average air temperature was approximately 3.7 ◦ C, with a seasonal range from −3.9 ◦ C in January to 10.7 ◦ C in July. The annual mean precipitation for 2004–2016 was 392.1 mm, varying from minimum of 239.5 mm in 2009 and maximum of 552.6 mm in 2008. In summer, the Indian summer monsoon (ISM) brings the majority of annual precipitation to the southern TP (approximately 90% occurs in May–September). 2.2. Precipitation isotope data A total of 480 precipitation samples were collected at two hydrological stations (Baidi and Wengguo) in the Yamdruk-tso basin from April 3, 2004 to July 24, 2016 (data from 2005, 2008, and 2009 were absent). The precipitation samples were collected at 20:00 as daily samples using a specifically designed container (based on the design of Groning et al., 2012), which can avoid the re-evaporation of samples. We recorded the relevant precipitation amounts (P) using a rain gauge. Totally, 408 daily samples were collected at Wengguo station from April 2004 to April 2016, and 127 daily samples at Baidi station from June 2012 to July 2016. All samples were conserved in plastic bottles and frozen below −15 ◦ C prior to analysis. The oxygen isotope ratios (δ 18 O) of 247 precipitation samples collected between June 2004 and November 2011 were measured in the Key Laboratory of Tibetan Environment Changes and Land Surface Processes using a MAT-253 mass spectrometer with an analytical precision of 0.2h. We measured both the oxygen isotope ratios (δ 18 O) and hydrogen isotope ratios (δ 2 H) of the remaining 233 samples collected between June 2012 and July 2016 using a Picarro-L2130i Cavity Ring-Down Spectroscopy (CRDS) with a precision of 0.1h and 0.6h for δ 18 O and δ 2 H, respectively. All measured results were scaled to the VSMOW (Vienna Standard Mean Ocean Water) by laboratory standard waters which determined by calibration against IAEA VSMOW and Standard Light Antarctic Precipitation water. In this study, the precipitation isotope data from the two stations were composited to one time series that was representative of the entire basin. For days (55 days) when both stations reported precipitation, we used the weighted average isotope values. Therefore, we have a total of 480 daily precipitation isotope data in the Yamdruk-tso basin for discussion in this paper. Of the total samples, 439 of them were collected during the monsoon months of May through September (MJJAS).

Fig. 2. δ 18 O and δ 2 H values in precipitation (δ 2 H) (open circles) from Yamdruk-tso basin during 2012–2016, the black dotted line represents the global meteoric water line and the blue line is the local meteoric water line.

defined as clouds up to 2 km altitude, middle clouds are from 2–4 km altitude, and high clouds are from 3–8 km altitude. National Oceanic and Atmospheric Administration (NOAA) interpolated OLR data for 2004–2016 were used as proxies for convection (Liebmann, 1996), and are available at https://www.esrl. noaa.gov/psd/data/gridded/data.interp_OLR.html. In order to trace the controlling mechanism of interannual variation of precipitation δ 18 O values, we used Southern Oscillation Index (SOI) calculated using the pressure differences between Tahiti and Darwin by the Australian Bureau of Meteorology. The calculation method and data are available at http://www.bom.gov.au/climate/current/ soihtm1.shtml. 3. Results 3.1. Local meteoric water line (LMWL) The LMWL is related to different moisture sources and/or different hydrological cycles. Fig. 2 plots the relationships between δ 2 H and δ 18 O values in the Yamdruk-tso basin based on the daily precipitation isotopic data during 2012–2016. This relationship, δ 2 H = 8.31δ 18 O + 10.04 (R2 = 0.97, p < 0.01) (Fig. 2), is very close to the global meteoric water line (δ 2 H = 8δ 18 O + 10, p < 0.01) (Craig, 1961). Compared to the LMWL in the northern part of the TP, the intercept is lower in southern TP station due to the direct influence of ocean evaporation vapor sources in the summer monsoon season (Tian et al., 2001a).

2.3. Meteorological data 3.2. Seasonal characteristics of precipitation isotopes We used local daily meteorological data, including relative humidity (RH), air temperature (T), and wind speed (WS) from the Langkazi meteorological station (28◦ 58 N, 90◦ 24 E, 4460 m) to evaluate the local controls on precipitation isotopes. ERA-Interim data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were also used to examine the large regional influence on precipitation isotopes in the southern TP. ERA-Interim data have a spatial resolution of 2.5◦ × 2.5◦ . Here, we used the total cloud cover (TCC), high cloud cover (HCC), medium cloud cover (MCC), and low cloud cover (LCC) data from 2004 to 2016 (available at https://apps.ecmwf.int/datasets/data/interimfull-daily/levtype = sfc/). Cloud cover (also known as cloudiness, cloudage, or cloud amount) refers to the fraction of the sky obscured by clouds when observed from a particular location. Cloud cover is correlated to sunshine duration as the least cloudy areas are the sunniest and vice versa. Cloud cover can be classified by altitude into low, middle, and high cloud, where low clouds are

To evaluate local influences on seasonal precipitation isotopes, we compared the daily precipitation δ 18 O and d-excess values in the basin with daily meteorological data from the Langkazi meteorological station (Fig. 3). Precipitation δ 18 O and d-excess values showed almost consistent seasonal variations during the observation period. Higher precipitation δ 18 O values were observed prior to the onset of the summer monsoon (May–June), followed by a decreasing trend until the end of the monsoon season in September. An approximately similar pattern was observed for precipitation d-excess values from 2012–2016, but the lowest d-excess value occurred in the mature monsoon months of July–August. Daily δ 18 O values ranged from −29.2h to 5.2h and d-excess values ranged from −34.3h to 34.2h, with both showing large daily variations. Large daily variations of precipitation d-excess are probably linked to the ocean surface climate (Merlivat and Jouzel, 1979),

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Fig. 3. Temporal variations of daily precipitation δ 18 O and d-excess values in Yamdruk-tso basin, and comparisons with Precipitation amount (P), Temperature (T) and Relative Humidity (RH) at Langkazi meteorological station.

but re-evaporation of raindrops under clouds may also exert an impact (He et al., 2018). Both precipitation δ 18 O and d-excess values in this study showed no consistent seasonal variation with air temperature nor humidity, in agreement with other studies on the southern TP ISM region (Guo et al., 2017; Tian et al., 2001b). 3.3. Relationship between precipitation δ 18 O and local meteorological data We also analyzed the relationship between precipitation δ 18 O values in the Yamdruk-tso basin and local meteorological parameters to evaluate their local influence. Fig. 4 shows the scatter plots of daily precipitation δ 18 O values with P, T, WS, and RH. A weak inverse correlation existed between precipitation δ 18 O and RH (Fig. 4d). Poor correlations were found between the basin scale precipitation δ 18 O and P (r = −0.11, and p < 0.05; Fig. 4a), T (r = −0.21, and p < 0.01; Fig. 4b), and WS (r = 0.19, and p < 0.01; Fig. 4c). These relationships indicated that the seasonal variations of precipitation isotopes were less dependent on local meteorological parameters, as was suggested for the Asian monsoon region in earlier studies (Guo et al., 2017; Tian et al., 2003). 4. Discussion 4.1. Relationship between precipitation δ 18 O, large regional cloud cover, and OLR Precipitation isotopes experience a combined effect from ocean surface evaporation, rainout during moisture transport in the upper stream, and precipitation processes (Aggarwal et al., 2004; Dansgaard, 1964). Increasingly, a consensus is being reached that atmospheric circulation has the greatest influence on precipitation isotopes (Breitenbach et al., 2010; Cai and Tian, 2016; Kurita, 2013; Rahul et al., 2016). Clouds play multiple critical roles in the climate systems and hydrologic cycles by impacting atmosphere and surface energy budgets (Watanabe et al., 2018). Stephens (2005) suggested that bulk precipitation efficiency and water cycle responses

to climate radiative forcing are likely controlled by cloud feedback. Condensation in the clouds forms precipitation and leads to isotope fractionation. Therefore, cloud cover, influenced by convective activity as an indicator of large-scale convergent zone movement and small-scale convective activity, is correlated to precipitation isotope variations. Hence, in order to evaluate these influences and reveal how large-scale atmospheric circulation might influence precipitation isotopes in the southwest TP, we calculated the correlations between basin-scale daily precipitation δ 18 O values in the Yamdruk-tso basin, regional cloud cover at different levels, and OLR. We calculated the spatial correlation of daily precipitation δ 18 O values in MJJAS for the Yamdruk-tso basin with TCC (Fig. 5a), HCC (Fig. 5b), MCC (Fig. 5c), and LCC (Fig. 5d) for the 5th day prior to precipitation events. The results showed significant correlations with both TCC and HCC, centered in the Indian subcontinent. However, the correlation with MCC and LCC were relatively weak. We defined the region with the most significant correlation between daily δ 18 O and cloud cover over the Indian subcontinent as the R1_Zone (Fig. 5a). In the R1_Zone, the correlation coefficient was r = −0.42 (p < 0.01) with TCC and r = −0.37 (p < 0.01) with HCC. This correlated region coincides with the heavy precipitation zone of strong convection in the monsoon season and is in the upper stream of moisture transport to the southern TP (Dong et al., 2016; Krishnamurthy and Shukla, 2007). The higher negative correlation with HCC than with MCC and LCC agrees with the frequent low pressure systems emerging in the northern Indian subcontinent during the summer monsoon season. These low pressure systems promote favorable mid-troposphere transport into the TP, while over 60% of summer precipitation over the southern TP is due to monsoon low pressure systems in that region (Dong et al., 2016). Precipitation isotopes record the signal of the process preceding the precipitation events. Generally, oceanic moisture sources are subject to a longer transport time before reaching the continent and condensing to precipitation. Thus, precipitation δ 18 O values are dependent on the moisture source signal and rainout from the upper stream of moisture transport a few days prior. Consequently,

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Fig. 4. Scatter plots of daily δ 18 O in Yamdruk-tso basin and daily (a) Precipitation amount (P), (b) Temperature (T), (c) Wind Speed (WS), and (d) Relative Humidity (RH).

Fig. 5. Spatial distribution of the correlation coefficients between daily precipitation δ 18 O in summer [May to September] for the Yamdruk-tso basin and the 5th day (prior to precipitation events) (a) total cloud cover (TCC), (b) high cloud cover (HCC), (c) medium cloud cover (MCC), and (d) low cloud cover (LCC) during 2004–2016. The 2.5◦ × 2.5◦ grids in (a) highlight the significant negative correlation regions (67.5–87.5◦ E and 15–27.5◦ N), termed the R1_Zone.

we defined dr (0−15d) as the period prior to the precipitation event, where dr = 0 represents the mean daily meteorological data on the precipitation day, where dr = 1, 2, 3. . .15 represent meteorological data on the 1st, 2nd, 3rd. . .15th day prior to the precipitation event. We calculated the correlation coefficients between daily δ 18 O and TCC over the R1_Zone for dr days (from 0 to 15) preceding each precipitation event. The result showed an increasingly negative correlation coefficient as dr varied from 0 to 4, and it remained at a stable high level when dr was between 4 and 10, then decreased with additional days. A previous study in the western TP also found the maximum correlation coefficient

between daily precipitation δ 18 O and OLR when dr was 4 to 5 (Guo et al., 2017), revealing a rather consistent average duration of moisture transport from the sensitive region to the precipitation site in the TP. Therefore, we analyzed the correlation coefficients between daily precipitation δ 18 O and climatic conditions over a large region on the 5th day prior to precipitation events (dr = 5). OLR describes the electromagnetic energy radiating out from the Earth as infrared radiation in a low energy atmosphere in the form of thermal radiation (Petty, 2006). In many previous studies, OLR has been specifically used to measure cloudiness, upstream convection, and rainfall over the tropics due to deep convection

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Fig. 6. Spatial correlation coefficients between δ 18 O in May through September for the Yamdruk-tso basin and outgoing longwave radiation (OLR) for the 5th day prior to precipitation events. The 2.5◦ × 2.5◦ grid represents the significant negative correlation region (R1_Zone) in Fig. 5. Table 1 Comparison of correlation coefficients between daily precipitation δ 18 O in Yamdruktso basin and TCC, HCC, MCC, LCC, OLR both on large-scale (dr = 5) over the R1_Zone and local-scale in the Langkazi station (dr = 0) in May through September during 2004–20016. Boldface values indicate the most significant correlation. Notes: TCC, HCC, MCC, LCC, OLR represent total, high, medium, low cloud cover and outgoing longwave radiation values, respectively. dr = 0 and 5 represent the mean daily meteorological data on the precipitation day and the 5th day prior to the precipitation event. Large regional scale

TCC HCC MCC LCC OLR

Local scale

δ 18 O

TCC

−0.42 −0.37 −0.18 −0.25

1.0 0.91 0.42 0.43 −0.64

0.32

Fig. 7. Spatial correlation between precipitation-weighted δ 18 O of Yamdruk-tso basin and total cloud cover in May through September (MJJAS) for 2004–2015, termed the parameters as δ 18 OMJJAS and TCCMJJAS respectively. The 2.5◦ × 2.5◦ grids highlight the significant negative correlation zone (47.5–75◦ E and 10–20◦ N and 60–75◦ E and 5–10◦ N), marked as the R2_Zone.

HCC 1.0 0.27 0.22 −0.64

δ 18 O

TCC

HCC

−0.22 −0.28 −0.07 −0.20

1.0 0.51 0.331 0.68 −0.42

1.0 0.20 0.25 −0.50

0.19

(Risi et al., 2008a). Here we first calculated the correlation between daily precipitation δ 18 O values in the Yamdruk-tso basin and daily OLR (dr = 5) (Fig. 6). The positive correlation between δ 18 O and OLR was most prominent over the Indian subcontinent, with correlation coefficient of r = 0.32 (p < 0.01) in the R1_Zone in MJJAS. The spatial correlation between precipitation δ 18 O and OLR was almost identical to that between δ 18 O and total cloud cover (Fig. 5a). Precipitation δ 18 O over other Asian monsoon regions are also positively correlated with OLR (He et al., 2018). In particular, the precipitation and vapor δ D over Lhasa in the southern TP is significantly correlated to OLR in the northern Indian subcontinent (Gao et al., 2013; He et al., 2015), indicating a consistent large-scale regional and atmospheric control of precipitation isotopes. A previous study also found that intense convection activity resulted in high level convective cloud cover, and stressed that the precipitation isotopologues recorded in natural archives from the southern TP could document past variations of ISM intensity (He et al., 2015). To identify the main factor among the key parameters related to precipitation δ 18 O, we performed a correlation analysis among daily precipitation δ 18 O in the Yamdruk-tso basin and averaged TCC, HCC, MCC, LCC, and OLR in R1_Zone (Table 1). In the largescale regional correlation analysis results, there was a significant positive correlation between TCC and HCC (r = 0.91, p < 0.01), indicating that clouds generally reached high altitude (3–8 km) in the R1_Zone. The precipitation δ 18 O values were poorly correlated with MCC and LCC, with coefficients of r = −0.18 and −0.25 (p < 0.01), respectively, lower than the correlation coefficients with TCC (r = 0.42, p < 0.01) and HCC (r =0.37, p < 0.01). TCC and HCC showed strong negative correlations with OLR, both have an r = −0.64 (p < 0.01), implying that the occur-

Fig. 8. Interannual variation of mean precipitation-weighted δ 18 O, total cloud cover over the R2_Zone, and Southern Oscillation Indexin in May through September (MJJAS) of each year for 2004-2015, termed the parameters as δ 18 OMJJAS , TCCMJJAS and SOIMJJAS respectively.

rence of high clouds were generally related to deep convection. The robust positive correlation between δ 18 O and OLR confirmed that δ 18 O values in summer monsoon precipitation were correlated with the intensity of convection (Bony et al., 2008; Cai et al., 2017; Guo et al., 2017; Liebmann, 1996; Risi et al., 2008a, 2008b). The correlations in Table 1, together with the consistent correlations of precipitation δ 18 O values with high cloud cover (Fig. 5b) and OLR (Fig. 6), demonstrated that high altitude condensation in convective precipitation depleted the residual vapor isotopes in the upper stream and subsequent precipitation, yielding the observed correlation between precipitation δ 18 O and regional HCC (and OLR). 4.2. Interannual variation of precipitation δ 18 O with cloud cover and SOI For the period MJJAS of each year for 2004–2015 (precipitation was not fully sampled in 2016), we performed an interannual correlation analysis between mean precipitation-weighted δ 18 O (δ 18 OMJJAS ) and mean TCC (TCCMJJAS ). The results showed a coherent correlation between summer precipitation δ 18 O and TCC on an interannual time scale (Fig. 7). The most correlated region was the main part of the Arabian Sea extending to the Somalia Peninsula (defined here as the R2_Zone). We further compared the precipitation δ 18 OMJJSA , TCCMJJAS averaged over the R2_Zone, and the SOI values of ENSO strength in MJJAS (SOIMJJAS ) from 2004 to 2015 (Fig. 8). The results showed a strong positive correlation between R2 TCCMJJAS and SOIMJJAS

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(r = 0.85, p < 0.01). The concurrent precipitation δ 18 OMJJAS values were also correlated with the SOIMJJAS values (r = −0.57, p = 0.85). Strong negative correlations also existed between precipitation δ 18 OMJJAS and R2 TCCMJJAS (r = −0.64, p < 0.05). In particular, 2006 and 2015 were El Niño warm episodes, with the most negative SOIMJJAS and lower R2 TCCMJJAS values, while the precipitation isotopes were higher. Conversely, 2010 was typical of a La Niña cold episode, with maximum values of both SOIMJJAS and R2 TCCMJJAS and minimum precipitation δ 18 OMJJAS values during the observation period. Previous studies have shown that ENSO plays a key role in modulating the longer-term interannual variation of δ 18 O in precipitation of the Asian monsoon region (Cai et al., 2017; Webster et al., 1998). The underlying mechanism is that the interannual variations of convective precipitation in the tropical Indo-Pacific region are associated with the ENSO cycle through Walker circulation, and enhanced convection in the source region will deplete vapor heavy isotopes and subsequent precipitation isotopes during cold La Niña years (Cai et al., 2017). The negative correlation between the archival isotope signal and the ENSO cycle has also been observed in ice cores (e.g. Shao et al., 2017), tree rings (e.g. Liu et al., 2017), and cave records (Yang et al., 2016). We note that the region sensitive to ice core isotopic composition in the central Himalayas was located on that range’s southern slope (Pang et al., 2014). However, in this study the equivalent region was located over the ocean’s surface. This discrepancy is probably related to the fact that the high elevations in the central Himalayas can receive up to 40–80% of their annual precipitation as snowfall during non monsoon season (Lang and Barros, 2004; Pang et al., 2014). Therefore, the stable isotopic record in Dasuopu Glacier is primarily controlled by both the westerlies and the Indian summer monsoon (Pang et al., 2014). Our study area had a higher summer monsoon precipitation ratio (up to 90%) and therefore was more correlated to the convective intensity, and thus the cloud cover, in the moisture source region over the ocean’s surface in summer. In addition, monsoon moisture trajectories of Indian summer monsoon transports water vapor from the Indian Ocean to the Himalayas and the southern Tibetan Plateau from the Indian Ocean across the Arabian Sea, then along the Indian River valley to the western Himalayas and Tibetan Plateau (Liu, 1989), which supports our identification of a correlation zone over the Arabian Sea. 4.3. Local and regional impact on precipitation δ 18 O in the southern TP In order to distinguish between the different effects of local and regional convection on precipitation isotopes in the southern TP, we compared the seasonal variations of OLR and precipitation δ 18 O on both the regional scale (the R1_Zone) and in the local Yamdruktso basin. The left panel of Fig. 9 shows the seasonal variations of OLR, TCC over the R1_Zone and precipitation δ 18 O in the Yamdruktso basin for selected years (2004, 2007, 2010, 2011) with sufficient precipitation days. The right panel of Fig. 9 is the same but for the local OLR and TCC at Langkazi meteorological station. These parameters showed roughly consistent trends in different years on the regional and local scales, though the difference between these was obvious. At the regional scale, OLR showed a strong seasonal signal with lower values in the summer monsoon season. At the local scale, the seasonal changes of OLR were much weaker, and large fluctuations in the monsoon season were probably due to local convection. The statistical analysis showed a linear correlation coefficient of r = 0.32 (p < 0.01) between precipitation δ 18 O and regional OLR in the Indian subcontinent, but a weaker correlation of r = 0.19 (p < 0.01) between precipitation δ 18 O and local OLR at Langkazi station. Seasonal TCC also showed different behaviors at the regional scale compared to the local scale. However, at the regional scale there was a strong seasonality of TCC, with sig-

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nificantly higher TCC in the summer monsoon season. Conversely, seasonal TCC at the local scale showed much less seasonality. This yielded a large difference in the statistical results of the correlation analysis. The correlation coefficient of r = −0.42 (p < 0.01) between precipitation δ 18 O and regional TCC was more significant (negative higher) than the coefficient for local TCC (r = −0.22, p < 0.01). Table 1 presents the comparison of the regional and local influences on precipitation δ 18 O in the southern TP according to their correlations with different levels of cloud cover and OLR. The results demonstrated that the correlations between precipitation δ 18 O and TCC, HCC, and OLR at the regional scale were more significant than those at the local scale. The same results were found for the correlation between precipitation and OLR, indicating that both large-scale and local atmospheric convections exerted their influences on precipitation isotopes in the southern TP, but the influence of regional convection was stronger than local convections. 4.4. Climate significance of isotope proxies in the southern TP Stable isotope records in the TP ice cores are assumed to be a proxy for local temperature (Thompson et al., 2000), but this is not supported by modeling work (Brown et al., 2006) and has been challenged by recent observations of modern precipitation isotopes, particularly in the southern TP in tropical and mid-latitude regions (Tian et al., 2003). Our current precipitation isotope data from the southern TP revealed a clear correlation with large-scale regional cloud cover at both seasonal and interannual scales. This finding disagreed with the local climate control suggested by precipitation isotope evidence. In other Asian monsoon regions, earlier studies also argued that the precipitation “amount effect” was not persistent in Indian monsoon precipitation (Breitenbach et al., 2010; Yang et al., 2016) and suggested regional atmospheric circulation as the primary driver instead (Rahul et al., 2016). Studies in Lhasa and Nyalam over the southern TP confirmed that integrated regional convective activity along upstream air mass trajectories play important roles in controlling precipitation δ 18 O on both individual event and seasonal scales (Gao et al., 2013; He et al., 2015). On an interannual scale, cloud top pressure anomalies in the central Indo-Pacific may be related to precipitation δ 18 O anomalies, highlighting the control of large-scale convection on precipitation δ 18 O in the Asian monsoon region (Cai and Tian, 2016). Furthermore, a number of studies hypothesized that depletion of precipitation δ 18 O during intense monsoons may be related to water vapor condensation altitude (temperature) (Deshpande et al., 2010; Tian et al., 2001a). Our results also showed a higher correlation between precipitation δ 18 O and high cloud cover than with low and mid cloud cover, confirming the strong influence of convective precipitation on precipitation isotopes in summer monsoon regions (Bony et al., 2008; Risi et al., 2008a). Tropical water isotope ratios tend to be lower when the convection is more “top-heavy” and when convection is deeper and more intense (Aggarwal et al., 2016; Torri et al., 2017). Tree ring cellulose δ 18 O values reconstructed from the southeast TP are more strongly related to local cloud cover (from local meteorological observations) than precipitation, relative humidity, or temperature (Shi et al., 2012, 2011). This result is supported by our precipitation isotope data from the Yamdruk-tso Basin over the southern TP, where total cloud cover is among the most important factors in relation to precipitation δ 18 O. However, in this study, we found a much higher negative correlation coefficient for the regional influence from the Indian subcontinent than for the local influence, suggested that regional influence was more dominant than local influence on water isotopes. However, this type of large-scale, regional correlation analysis was not performed for the cellulose δ 18 O records.

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Fig. 9. Seasonal variations of precipitation δ 18 O in Yamdruk-tso basin as well as (a) large-scale total cloud cover (TCC) and outgoing longwave radiation (OLR) over the R1_Zone and (b) local TCC and OLR in 2004, 2007, 2010, and 2011.

In addition, we found different maximum correlation zones for the seasonal and interannual relationships between precipitation isotopes and TCC, which likely indicates different controlling mechanisms. We note that the sensitive regions identified at the seasonal and interannual scales exerted different effects on precipitation isotopes. In our seasonal relation analysis, precipitation isotopes were sensitive to cloud cover in the region centered in the Indian subcontinent, whereas the interannual scale found a correlated region over the ocean. This difference suggested variable

mechanisms controlling precipitation isotopes in the ISM region. This seasonal correlation was established due to enhanced depletion of heavy isotopes in the southern Himalayas in the summer monsoon season (He et al., 2015), while precipitation isotopes can integrate regional upstream convective activity and precipitation processes (Gao et al., 2013). However, in the interannual correlation analysis, the maximum correlation zone is over the Indian Ocean (the majority of the Arabian Sea and extending to the Somali Peninsula). As discussed above, the interannual variations of

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precipitation isotopes in the Asian monsoon region were strongly related to the ENSO cycle, with an underlying mechanism that identifies the importance of the intensity of convection precipitation in the source region over the tropical ocean influening the interannual changes in precipitation isotopes. This process is also supported by ice core isotope records from the middle TP that were negatively correlated with cloud top height over the tropical Indian Ocean (Shao et al., 2017). This finding also implied that the relationship established at seasonal scale will probably not exist at long time scales. As our data set is temporally limited to 10 years, it remains necessary that future research will require much longer time scale records from various archives. 5. Conclusions We conducted an analysis of the seasonal and interannual variations of precipitation δ 18 O in the southern TP, and determined their relationship with regional-scale cloud cover to improve the understanding of the paleoclimate significance preserved in various isotope archives. We first determined the local dependence of precipitation δ 18 O values. A weak “temperature effect” and a weak precipitation “amount effect” were found in precipitation isotopes, which agreed with earlier argument of precipitation isotopes as temperature proxy. Here, we focused on the relationship between precipitation isotopes and regional and local cloud cover in response to the early findings in the paleo archives. Larger-scale regional correlation analysis with cloud cover showed strong consistency in both the seasonal and interannual relationships, supporting the results of a previous reconstruction of cloud cover history from tree ring cellulose δ 18 O values over the southern TP. However, our results showed a higher correlation with large regional cloud cover than local cloud cover, highlighting the influence of large regional convection on precipitation isotopes. In addition, we also showed that cloud cover and OLR (proxies for convective intensity) in the Indian subcontinent were significantly correlated, specifically identified a higher correlation between precipitation isotopes and higher level cloud cover, reconfirming the strong influence of intensive convection. We identified different maximum correlation zones for the seasonal and interannual relationships; i.e., in the Indian subcontinent for the seasonal correlation and in the main part of the Arabian Sea and extending to the Somalia Peninsula for the interannual correlation. We explained this by considering different mechanisms for producing the observed seasonal and interannual precipitation isotope changes. In addition, we observed consistent changes in annual precipitation δ 18 O and SOI, confirming the role of the ENSO cycle on regional precipitation isotopes. This study will improve our knowledge of the drivers of the seasonal and interannual precipitation isotopes and throw light on the paleoclimate reconstructions using isotope archives over the southern TP. Acknowledgements The authors gratefully acknowledge NOAA Air Resources Laboratory (ARL) for provision of the OLR and SOI data. The ECMWF Public Datasets web interface provided the ERA-Interim data. We are also grateful to the China Meteorological Administration for provision of local daily meteorological data. Paul Schuster from USGS has made suggestions and improvement on writing the full text. This work was funded by the National Natural Science Foundation of China (Grant 41771043, 41661144044, 41530748), special grants of China Postdoctoral Science Fund (Grant 2018T110145), and Yunnan University’s Research Innovation Fund for Graduate Students (Grant 2018Z098).

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